Abstract: SPatial Analysis of Cardiovascular Events (SPACE) in the Golestan Cohort Study Cardiovascular disease (CVD) is the leading cause of death in the world, with 80% of global CVD deaths occurring in low- and middle-income countries (LMICs). In addition to the epidemiologic burden, CVD imposes a significant economic burden. A growing literature demonstrates that several environmental variables strongly predict CVD events. New spatial statistical methods can determine the geographic distribution and magnitude of effect of these environmental variables, enabling the design of spatially-targeted interventions for CVD prevention. To date, however, most studies are conducted in high-income settings and focused on a single geospatial variable. The Golestan Cohort Study (GCS) is an ongoing prospective study in Iran, initially designed to identify risk factors for esophageal cancer via a comprehensive assessment of multiple potential risk factors. In 2004, the GCS enrolled approximately 50,000 men and women ages 40-75 across urban and rural Golestan, Iran, and participants are followed up actively every 12 months. Since inception, the GCS has registered several thousand validated CVD deaths. Nested within the GCS is the PolyIran Cohort, a cluster randomized controlled trial testing the efficacy of a polypill for prevention of CVD. The PolyIran Cohort includes over 8,000 subjects monitored closely for specific major adverse CVD events such as myocardial infarction (fatal or non-fatal), unstable angina, sudden death, heart failure, coronary artery revascularization procedures, and stroke (fatal or non-fatal). These data provide an unprecedented opportunity to test the hypothesis that spatial risk factors (SRFs) are independently associated with the incidence of adverse CVD events in the GCS, and to use these SRFs to generate and validate a spatial model that predicts the incidence of CVD events in an LMIC setting. Aim 1 is to combine existing GCS datasets, maps, and satellite data to generate SRF surfaces for ambient air pollution, population density, land cover, proximity to health centers, proximity to major roadways, and socioeconomic environment. We will then use a Cox proportional hazards model to estimate associations between these SRFs and the incidence of CVD mortality using both village-level and individual- level (Subsidiary Aim 1.1) geo-coded data. We will use a Bayesian spatial random effects survival model (spatial frailty model) to adjust for spatial dependence in the data. Subsidiary Aim 1.2 will validate the spatial survival model that has been developed in Aims 1 and 1.1, using a validation sub-set of the GCS that will not be used for the derivation of the model. Aim 2 will use the same spatial survival approach, applied to the control arm of the PolyIran cohort, in order to identify SRFs independently associated with the incidence of major adverse CVD events (listed above), using both village-level and individual-level geo-coded data. Through this project, we aim to add to the existing knowledge of risk for CVD and other chronic diseases in low-resource settings worldwide, using an innovative geospatial approach.